pandas.merge_ordered

pandas.merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=('_x', '_y'), how='outer')[source]

Perform merge with optional filling/interpolation designed for ordered data like time series data. Optionally perform group-wise merge (see examples)

Parameters
leftDataFrame
rightDataFrame
onlabel or list

Field names to join on. Must be found in both DataFrames.

left_onlabel or list, or array-like

Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns

right_onlabel or list, or array-like

Field names to join on in right DataFrame or vector/list of vectors per left_on docs

left_bycolumn name or list of column names

Group left DataFrame by group columns and merge piece by piece with right DataFrame

right_bycolumn name or list of column names

Group right DataFrame by group columns and merge piece by piece with left DataFrame

fill_method{‘ffill’, None}, default None

Interpolation method for data

suffixesSequence, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

Changed in version 0.25.0.

how{‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘outer’
  • left: use only keys from left frame (SQL: left outer join)

  • right: use only keys from right frame (SQL: right outer join)

  • outer: use union of keys from both frames (SQL: full outer join)

  • inner: use intersection of keys from both frames (SQL: inner join)

New in version 0.19.0.

Returns
mergedDataFrame

The output type will the be same as ‘left’, if it is a subclass of DataFrame.

See also

merge
merge_asof

Examples

>>> A                      >>> B
      key  lvalue group        key  rvalue
0   a       1     a        0     b       1
1   c       2     a        1     c       2
2   e       3     a        2     d       3
3   a       1     b
4   c       2     b
5   e       3     b
>>> merge_ordered(A, B, fill_method='ffill', left_by='group')
  group key  lvalue  rvalue
0     a   a       1     NaN
1     a   b       1     1.0
2     a   c       2     2.0
3     a   d       2     3.0
4     a   e       3     3.0
5     b   a       1     NaN
6     b   b       1     1.0
7     b   c       2     2.0
8     b   d       2     3.0
9     b   e       3     3.0
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